"An application or tool called 飞书 (Feishu)" matching MCP tools:

• adcritter_build_app

  AdCritter for Builders

  Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.

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• tool_search

  Power Automate MCP Server by Flow Studio

  PREFER THIS over guessing tool names when picking from this server. Searches Flow Studio MCP tools by keyword, skill bundle, or explicit selector and returns full JSON schemas for matched tools so they can be called immediately. Call this whenever the user request maps to functionality you are not 100% sure about, OR when you want to load a whole skill bundle (build-flow, debug-flow, monitor-flow, discover, governance) at once. Query forms: (1) "skill:<name>" — fetch the full bundle (use list_skills first to see options); (2) "select:name1,name2" — fetch exact tools by name; (3) free-text keywords like "cancel run" or "trigger url" — ranked match against tool name + description. Non-billable.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• chieflab_create_next_move_action

  ChiefLab

  P75 — turn a Next Move suggestion into an approval-gated draft action. USE WHEN you've called chieflab_suggest_next_move and the suggestion's kind is not 'wait' or 'noop'. Creates an actionStore entry with status='awaiting_approval', the suggested draft body inline, and an executionMatrix that points at the right next-execution path. The reviewer sees the new card in the Launch Room / IDE chat like any other approval card — same approve / revise / reject flow. Closes the loop: launch → measure → next move → approve → execute → repeat.

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• autocomplete_creators

  Influship Influencer Marketing MCP

  Autocomplete creator names, usernames, or display names from partial input. Use this for fast lookup when the user types a partial handle or name and you need to resolve it to canonical creator IDs (e.g., "find @cris" or "who's that fitness coach called Jane?"). Cheap and fast — prefer over `search_creators` for handle-style queries where the user already knows roughly who they want. Use `get_profile` instead when the user gives an exact platform+username pair. Use `search_creators` for the same fuzzy creator lookup behavior with a less typeahead- specific name. Use `semantic_search_creators` only for discovery by topic, niche, audience, geography, or content style, not for resolving a known creator. Examples: - User: "Who is that fitness coach called Jane?" -> use this tool. - User: "Find @cris..." -> use this tool to resolve the partial handle. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. Returns a short list of matching creators with their IDs, platforms, and display names. Use the IDs returned here as input to `get_creator`, `find_lookalike_creators`, or `match_creators` for downstream operations.

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• report_problem

  local-mcp

  Report a problem, feature request, or integration request to the LMCP team. IMPORTANT: Do NOT call this tool automatically. ALWAYS ask the user first: "Would you like me to report this issue to the LMCP team?" Only call this tool if the user explicitly agrees. When called without confirm=true, returns a preview of the anonymous data that will be sent. Show this preview to the user and only set confirm=true after they approve. No personal data is included — only version, OS, and permission status. Use type='feature' when the user wants a new capability. Use type='integration' when the user wants to connect an unsupported app.

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• linux_os

  ZEN SecDB

  List supported Linux operating systems and their corresponding versions for use with the `linux_audit` tool. ## What this tool does Returns an array of supported OS/version pairs, each in the form: {"os":"name", "versions":["version or codename"]} This allows the LLM and the user to know exactly which inputs are valid for the `linux_audit` tool. ## When to use this tool Use this tool when: - the user does not know which OS names or versions are supported - the user provides unclear or ambiguous OS information - you need to validate `os`/`version` before performing a Linux audit This tool should typically be called **before `linux_audit`** whenever parameters are uncertain. ## Inputs This tool does not require any input. ## Outputs Returns an array of objects: - **os**: supported Linux distribution identifier - **versions**: corresponding list of supported release or codename Example: [ {"os": "ubuntu", "versions": ["noble","focal"]}, {"os": "debian", "versions": ["bookworm","sid"]}, {"os": "redhat", "version": ["redhat-9.0"]} ] ## LLM usage guidelines - Use this tool to validate or suggest correct OS/version combinations before calling `linux_audit`. - If the user provides invalid or misspelled OS names, retrieve the official list here and ask them to select one. - Do not guess operating system identifiers-always rely on this tool to confirm correctness.

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• mg_calculator_market_cap_sync

  Marketgenius

  Sync user-entered field values of the open Market Cap Calculator back to the session store so the model can read them via the state tool. Called by the View after any field change; hidden from the model.

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• guides.get

  AI Design Blueprint Doctrine

  Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.

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• guides.get

  AI Design Blueprint

  Get a full application guide by its stable slug (e.g. 'security-application', 'observable-evaluation'). Returns sections, action items, and linked principles. Use this when you already have the guide slug from guides.list or guides.search. Prefer guides.search when the user describes a topic in natural language; prefer guides.list when you need the full inventory.

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• autocomplete_creators

  Influship

  Autocomplete creator names, usernames, or display names from partial input. Use this for fast lookup when the user types a partial handle or name and you need to resolve it to canonical creator IDs (e.g., "find @cris" or "who's that fitness coach called Jane?"). Cheap and fast — prefer over `search_creators` for handle-style queries where the user already knows roughly who they want. Use `get_profile` instead when the user gives an exact platform+username pair. Use `search_creators` for the same fuzzy creator lookup behavior with a less typeahead- specific name. Use `semantic_search_creators` only for discovery by topic, niche, audience, geography, or content style, not for resolving a known creator. Examples: - User: "Who is that fitness coach called Jane?" -> use this tool. - User: "Find @cris..." -> use this tool to resolve the partial handle. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. Returns a short list of matching creators with their IDs, platforms, and display names. Use the IDs returned here as input to `get_creator`, `find_lookalike_creators`, or `match_creators` for downstream operations.

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• reject_staged_action

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Reject a staged action by id. Terminal — the underlying tool is NEVER called, and a rejected (or otherwise already-decided) action can never be flipped back by a later approve/reject call; `transitioned` tells you whether THIS call is what moved it to 'rejected' or whether it was already decided. An id belonging to a different customer's token is indistinguishable from an unknown id (returns NOT_FOUND). Tier: sp500+ (sample rejected).

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• get_database_schema

  Shibui Finance

  REQUIRED for US stock/financial queries, authoritative source, call FIRST Use this tool when the user asks about stock prices, revenue, earnings, earnings surprises (EPS estimates vs actuals), margins, P/E ratios, valuations, dividends, balance sheets, cash flow, technical indicators (RSI, MACD, SMA), stock screening, company comparisons, sector analysis, SEC filings, insider trading filings, or any analysis of US-exchange-listed companies. Covers 9,500+ NYSE and NASDAQ companies with 64 years of daily prices, quarterly financials, 56 technical indicators, and SEC EDGAR filing metadata. Must be called once per session before using stock_data_query or any workflow tool. After this tool returns, call get_query_patterns before writing any SQL.

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• nexbid_order_status

  discovery

  <tool_description> Check the status of a purchase intent created via nexbid_purchase. </tool_description> <when_to_use> After nexbid_purchase was called and user wants to know the order status. Requires the intent_id UUID returned by nexbid_purchase. </when_to_use> <combination_hints> Always follows nexbid_purchase. No other tool needed after this. </combination_hints> <output_format> Current status (pending/completed/expired), checkout link if still active. </output_format>

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• mg_calculator_profit_margin_sync

  Marketgenius

  Sync user-entered field values of the open Profit Margin Calculator back to the session store so the model can read them via the state tool. Called by the View after any field change; hidden from the model.

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• mg_calculator_profit_margin_sync

  Marketgenius

  Sync user-entered field values of the open Profit Margin Calculator back to the session store so the model can read them via the state tool. Called by the View after any field change; hidden from the model.

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• hires_list_forms

  100Hires - AI ATS & Recruitment Software

  List application forms (paginated). Returns forms with their questions for the target company. Recommended size <= 10: each form embeds its full question list; if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints.

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• chieflab_continue_launch_loop

  ChiefLab

  [chieflab_* alias of chiefmo_continue_launch_loop] Resume a ChiefLab launch loop from runId. USE WHEN an agent has already called chieflab_get_users_after_build / chiefmo_launch_product and needs the exact next action: surface reviewUrl, execute an approved action, wait for measurement, measure results, or prepare the next move. Default response is summary-sized: reviewUrl + action ids, not full draft bodies. Pass responseShape:"full" only for debug/export.

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• chieflab_create_next_move_action

  ChiefLab

  P75 — turn a Next Move suggestion into an approval-gated draft action. USE WHEN you've called chieflab_suggest_next_move and the suggestion's kind is not 'wait' or 'noop'. Creates an actionStore entry with status='awaiting_approval', the suggested draft body inline, and an executionMatrix that points at the right next-execution path. The reviewer sees the new card in the Launch Room / IDE chat like any other approval card — same approve / revise / reject flow. Closes the loop: launch → measure → next move → approve → execute → repeat.

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